Procedural outline

Turn on machines / heaters. Put mice in tailcuff room and let the room and mice acclimate to appropriate temperature for ~30-60 mins. Then, check cuffs for leaks. Put mice into restraints and perform 5 acclimation cycles + 20 recorded cycles.

When placing mice into restraints,
1. Quickly place nose cuff on to avoid letting them turn around in the restraint
2. Make sure that you can see them breathing…
3. Maximize tightness of fit and breathing

Download excel data after finishing the experiments onto thumb drive

Copy the data into a master excel file with two sheets, making sure that there’s only one header (at the very top of the page).

  1. You’ll need to add in two columns manually to this master sheet: Date and Phase. Phase can take one of 4 values: “training”, “baseline”, “vehicle”, “treatment”. Training data ultimately gets removed, but included in the data sheet for completeness. First sheet should look like this:
    Metadata
    Specimen Name Systolic Mean Rate Cycle # Date Phase
    M1 90 71 711 6 2021-03-09 baseline
    M1 110 80 603 7 2021-03-09 baseline
    M1 130 86 629 9 2021-03-09 baseline
    M1 94 72 693 10 2021-03-09 baseline
    M1 97 73 624 12 2021-03-09 baseline
    M1 94 72 637 13 2021-03-09 baseline
  2. Second sheet should be created manually based on your mice. Fill in the various fields.
    Metadata
    Specimen Name Notch Old Cage ID New Cage ID DOB Body weight (g) Date Status Date of death Machine ID
    M1 RN 643254 655606 2020-12-14 27.1 2021-03-05 Alive NA 1
    M2 XX 643254 655606 2020-12-14 NA 2021-03-05 Dead 2021-03-05 1
    M3 NN 643254 655606 2020-12-14 25.9 2021-03-05 Alive NA 1
    M4 DR 643240 655607 2020-12-15 28.4 2021-03-05 Alive NA 1
    M5 LN 643240 655607 2020-12-15 27.4 2021-03-05 Alive NA 1
    M6 BN 643240 655607 2020-12-15 26.9 2021-03-05 Alive NA 1
    M7 NN 643253 655605 2020-12-18 25.5 2021-03-05 Alive NA 2
    M8 BN 643253 655605 2020-12-18 24.2 2021-03-05 Dead 2021-03-13 2
    M9 DR 643243 655604 2020-12-17 26.5 2021-03-05 Alive NA 2
    M10 RN 643243 655604 2020-12-17 25.4 2021-03-05 Alive NA 2

Metadata Analysis

This workbook is set up to analyze two groups of mice! Just run and enjoy (you’ll probably need to change out drug names…)

Metadata
group Specimen Name Notch Old Cage ID New Cage ID DOB Status Date of death Machine ID Average body weight (g)
sunitinib M1 RN 643254 655606 2020-12-14 Alive NA 1 25.02143
vehicle M3 NN 643254 655606 2020-12-14 Alive NA 1 23.59286
vehicle M4 DR 643240 655607 2020-12-15 Alive NA 1 27.27857
sunitinib M5 LN 643240 655607 2020-12-15 Alive NA 1 26.07143
sunitinib M6 BN 643240 655607 2020-12-15 Alive NA 1 25.87857
sunitinib M7 NN 643253 655605 2020-12-18 Alive NA 2 24.00714
vehicle M9 DR 643243 655604 2020-12-17 Alive NA 2 25.82857

Inspect accepted cycles and changes in mouse body weight over time

### Average animal body weight

Animal body weight change over time

Blood Pressure Data Analysis

Filtering out days that had less than ‘x’ cycles

Removed days/Specimens with less than 5 cycles:
Specimen Name Date group # cycles reason
M1 2021-03-12 sunitinib NA bladder explosion
M7 2021-03-19 sunitinib 4 Low cycle count
M9 2021-03-19 vehicle 1 Low cycle count
M4 2021-04-04 vehicle NA leaky bladder

Removing outliers

Detect outliers using boxplot methods. Boxplots are a popular and an easy method for identifying outliers. There are two categories of outlier: (1) outliers and (2) extreme points. Values above Q3 + 2xIQR or below Q1 - 2xIQR are considered as outliers. Q1 and Q3 are the first and third quartile, respectively. IQR is the interquartile range (IQR = Q3 - Q1). This method is more robust than STDEV based outlier detection because outliers can skew the mean and STDEV of a sample.

Here, outliers are nominated based on daily blood pressure recordings, so as to not throw out data on treatment days when the blood pressure is expected to rise above the average.

Specimens by # of outliers
Specimen Name # Outliers
M1 17
M3 23
M4 5
M5 20
M6 20
M7 18
M9 14

Plot the data over time and visualize the variance per day, per sample with boxplots, over all days

Starting 03/05/21 - oral gavage and tailcuff training begins

Starting 03/08/21 - baseline recording before vehicle treatment

Starting 03/11/21 - vehicle treatment (10% PEG/ 0.5% Tween /~90% DI H2O at pH ~3.5) begins to establish baseline BP

Starting 03/15/21 - Randomized to Sunitinib (40 mg/kg/d) treatment or vehicle begins

Since blood pressure is always measured before oral gavage, baseline goes from 03/08 to 03/11, but vehicle treatment starts on 03/11. Observed effect of treatment is then on 03/12!!

Assess BP of randomized groups before beginning treatment

Plotting average difference between groups across phases

Differences are calculated as change per specimen over each phase

Time-series data

For completeness, here is the time series data of each mouse across each Phase of the experiment:

Time-series diff

Representing the data in another way…

Treatment with Sunitinib start on 03/15/21 BP (recording of effect starts on 03/16/21)

Since BP is recorded before gavage to reduce stress

These are the average blood pressures of each mice across each Phase of the experiment